用大模型每天自动构建会随时间演化的医学知识图谱
MedKGent: A Large Language Model Agent Framework for Constructing Temporally Evolving Medical Knowledge Graph
- 设计双智能体框架，每日从海量文献中提取并整合医学知识
- 构建含156,275实体、297万三元组的全球最大医用药学知识图谱
- 支持动态演化，提升医疗问答等任务的准确率，适合临床科研人员
医学文献的快速扩张给领域知识的规模化结构化带来挑战。知识图谱（KG）提供了解决方案，但现有构建方法缺乏泛化能力，且忽略知识的时序动态性。为此，我们提出MedKGent——一个基于大语言模型（LLM）的智能体框架，用于构建时序演化的医学知识图谱。利用1975至2023年间超过1000万篇PubMed摘要，MedKGent通过两个专用智能体每日增量构建知识图谱：提取器智能体识别知识三元组并赋信心评分，构造器智能体将三元组融入时序图谱，强化重复知识并解决冲突。最终生成的图谱包含156,275个实体和2,971,384个三元组，据我们所知是目前最大的由大模型生成的医学知识图谱。自动化与专家评估显示三元组有效性接近90%。在下游任务中，MedKGent-KG显著提升了五个大模型在七项医学问答基准上的检索增强生成性能。这些结果表明，MedKGent为医学知识表示与文献驱动的人工智能研究提供了可扩展、时序感知的基础架构。
原文摘要 · Abstract (English)
The rapid expansion of medical literature challenges the scalable structuring of domain knowledge. Knowledge Graphs (KGs) offer a solution, yet current construction methods lack generalizability and ignore the temporal dynamics of evolving knowledge. To address this, we introduce MedKGent, a Large Language Model (LLM) agent framework for building temporally evolving medical KGs. Using over 10 million PubMed abstracts from 1975 to 2023, MedKGent incrementally constructs a KG daily via two specialized agents. The Extractor Agent identifies knowledge triples and assigns confidence scores, while the Constructor Agent integrates these triples into a temporal graph, reinforcing recurring knowledge and resolving conflicts. The resulting KG contains 156,275 entities and 2,971,384 triples, making it, to our knowledge, the largest LLM-derived medical KG to date. Automated and expert assessments showed triple-validity rates approaching 90%. In downstream evaluations, MedKGent-KG significantly improved retrieval-augmented generation for five LLMs across seven medical question-answering benchmarks. Together, these results position MedKGent as a scalable and temporally aware infrastructure for medical knowledge representation and literature-grounded AI research.
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